Metaverse describes a new shape of cyberspace and has become a hot-trending word since
2021. There are many explanations about what Meterverse is and attempts to provide a …

Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality
tabular data for model training remains a significant obstacle. Numerous works have …

Traditional cost-based optimizers are efficient and stable to generate optimal plans for
simple SQL queries, but they may not generate high-quality plans for complicated queries …

Finding line-chart images similar to a given line-chart image query is a common task in data
exploration and image query systems, eg finding similar trends in stock markets or medical …

Given a dataset with incomplete data (eg, missing values), training a machine learning
model over the incomplete data requires two steps. First, it requires a data-effective step that …

The growing importance of data visualization in business intelligence and data science
emphasizes the need for tools that can efficiently generate meaningful visualizations from …

Discovering tables from poorly maintained data lakes is a significant challenge in data
management. Two key tasks are identifying joinable and unionable tables, crucial for data …

Successful machine learning (ML) needs to learn from good data. However, one common
issue about train data for ML practitioners is the lack of good features. To mitigate this …

In this paper, we study how to acquire labeled data points from a large data pool to enrich a
training set for enhancing supervised machine learning (ML) performance. The state-of-the …

Relational databases are extensively utilized in a variety of modern information system
applications, and they always carry valuable data patterns. There are a huge number of data …